标题： 对抗性医院不变特征学习用于WSI切片分类 显示英文标题

标题： Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification

摘要： 病理学基础模型（PFMs）在全幻灯片图像（WSI）诊断中表现出显著的潜力。 然而，不同医院的病理图像由于扫描硬件和预处理风格的差异而有所不同，这可能导致PFMs无意中学到医院特有的特征，对临床部署构成风险。 在这项工作中，我们提出了对因医院来源特征而产生的PFM领域偏差的首次系统研究。 具体而言，我们（1）构建了一个用于量化PFM领域偏差的流程，（2）评估和比较了多个模型的性能，并（3）提出了一种轻量级的对抗框架，从冻结的表示中移除潜在的医院特有特征，而无需修改编码器本身。 通过引入一个可训练的适配器和一个通过梯度反转层（GRL）连接的领域分类器，我们的方法学习任务区分但领域不变的表示。 在多中心组织病理学数据集上的实验表明，我们的方法在保持或甚至提高疾病分类性能的同时，显著降低了领域可预测性，特别是在域外（未见过的医院）场景中。 进一步的分析，包括医院检测和特征空间可视化，证实了我们的方法在减轻医院偏差方面的有效性。 我们将根据接受情况提供我们的代码。 显示英文摘要

摘要： Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals often vary due to differences in scanning hardware and preprocessing styles, which may lead PFMs to inadvertently learn hospital-specific features, posing risks for clinical deployment. In this work, we present the first systematic study of domain bias in PFMs arising from hospital source characteristics. Specifically, we (1) construct a pipeline for quantifying domain bias in PFMs, (2) evaluate and compare the performance of multiple models, and (3) propose a lightweight adversarial framework that removes latent hospital-specific features from frozen representations without modifying the encoder itself. By introducing a trainable adapter and a domain classifier connected through a gradient reversal layer (GRL), our method learns task-discriminative yet domain-invariant representations. Experiments on multi-center histopathology datasets demonstrate that our approach substantially reduces domain predictability while maintaining or even improving disease classification performance, particularly in out-of-domain (unseen hospital) scenarios. Further analyses, including hospital detection and feature space visualization, confirm the effectiveness of our method in mitigating hospital bias. We will provide our code based on acceptance.